Change detection is a popular and important image processing process with numerous practical applications such as medical imaging, environment, agriculture, and other remote sensing tasks. There are, however, some significant challenges confronting most change detection systems. The first challenge is image registration. For example, two images—a first image may be referred to as a reference image and a second image may be referred to as a test image in a change detection system or algorithm—are generated at different times. The reference image and the test image, however, are usually not aligned with each other, since the imaging platform is generally not retained in precisely the same exact geometry from one run to another. Performance of change detection often depends on how well image registration can be achieved. Misalignment between the reference image and the test image results in a translation, and even slight rotation, between the reference image and the test image. Although many change detection systems have been developed, the image registration process is typically performed separately (e.g., performed manually). Unfortunately, the performance of a change detection system often depends on how well image registration can be achieved. If alignment was not well performed prior to the application of the change detection algorithm, the resulting change detection performance will be significantly degraded. In addition, the degree of misalignment between the two images usually locally varies throughout large areas.
The second challenge confronting change detection systems relates to the suppression of image signatures from common objects, which appear in both the test and reference image scenes. For example, in an ideal situation, after the two images have been carefully registered, the image signatures of objects present in both the test and reference image would be almost identical. Consequently, the difference in image signatures between the test and reference image should be very small. In other words, the image signatures from common objects that appear in both the test and reference image would be suppressed by the change detection system. In current change detection systems, however, image signatures of the same object are somewhat different in the two images due to many reasons (e.g., calibration problem, slight change in aspect angle, noise, etc.) Differences in image signatures of a common object can lead to false positives since they falsely indicate large difference signatures in the resulting change detection image. These signature differences would be falsely declared as targets of interest (i.e., generate false alarms). Current state-of-the-art change detection algorithms often have difficulty in suppressing these differences since they simply detect any anomalies between the signatures of the objects which are common to the test and reference images.